plotVolcano {OUTRIDER} | R Documentation

## Visualization functions for OUTRIDER

### Description

The OUTRIDER package provides mutliple functions to visualize the data and the results of a full data set analysis.

This is the list of all plotting function provided by OUTRIDER:

- plotAberrantPerSample()

- plotVolcano()

- plotExpressionRank()

- plotQQ()

- plotExpectedVsObservedCounts()

- plotCountCorHeatmap()

- plotCountGeneSampleHeatmap()

- plotSizeFactors()

- plotFPKM()

- plotExpressedGenes()

- plotDispEsts()

- plotPowerAnalysis()

- plotEncDimSearch()

For a detailed description of each plot function please see the details. Most of the functions share the same parameters.

### Usage

    plotVolcano(
      ods,
      sampleID,
      main,
      padjCutoff = 0.05,
      zScoreCutoff = 0,
      pch = 16,
      basePlot = FALSE,
      col = c("gray", "firebrick")
    )

    plotQQ(
      ods,
      geneID,
      main,
      global = FALSE,
      padjCutoff = 0.05,
      zScoreCutoff = 0,
      samplePoints = TRUE,
      legendPos = "topleft",
      outlierRatio = 0.001,
      conf.alpha = 0.05,
      pch = 16,
      xlim = NULL,
      ylim = NULL,
      col = NULL
    )

    plotExpectedVsObservedCounts(
      ods,
      geneID,
      main,
      basePlot = FALSE,
      log = TRUE,
      groups = c(),
      groupColSet = "Set1",
      ...
    )

    plotExpressionRank(
      ods,
      geneID,
      main,
      padjCutoff = 0.05,
      zScoreCutoff = 0,
      normalized = TRUE,
      basePlot = FALSE,
      log = TRUE,
      col = c("gray", "firebrick"),
      groups = c(),
      groupColSet = "Accent"
    )

    plotCountCorHeatmap(
      ods,
      normalized = TRUE,
      rowCentered = TRUE,
      rowGroups = NA,
      rowColSet = NA,
      colGroups = NA,
      colColSet = NA,
      nRowCluster = 4,
      nColCluster = 4,
      main = "Count correlation heatmap",
      basePlot = TRUE,
      nBreaks = 50,
      show_names = c("none", "row", "col", "both"),
      ...
    )

    plotCountGeneSampleHeatmap(
      ods,
      normalized = TRUE,
      rowCentered = TRUE,
      rowGroups = NA,
      rowColSet = NA,
      colGroups = NA,
      colColSet = NA,
      nRowCluster = 4,
      nColCluster = 4,
      main = "Count Gene vs Sample Heatmap",
      bcvQuantile = 0.9,
      show_names = c("none", "col", "row", "both"),
      nGenes = 500,
      nBreaks = 50,
      ...
    )

    plotAberrantPerSample(
      ods,
      main = "Aberrant Genes per Sample",
      outlierRatio = 0.001,
      col = "Dark2",
      yadjust = 1.2,
      ylab = "#Aberrantly expressed genes",
      ...
    )

    plotFPKM(ods, bins = 100)

    ## S4 method for signature 'OutriderDataSet'
    plotDispEsts(
      object,
      compareDisp,
      xlim,
      ylim,
      main = "Dispersion estimates versus mean expression",
      ...
    )

    plotPowerAnalysis(ods)

    plotEncDimSearch(ods)

    plotExpressedGenes(ods, main = "Statistics of expressed genes")

    plotSizeFactors(ods, basePlot = TRUE)

### Arguments

`ods, object` | An OutriderDataSet object.
`sampleID, geneID` | A sample or gene ID, which should be plotted. Can also be a vector. Integers are treated as indices.
`main` | Title for the plot, if missing a default title will be used.
`padjCutoff, zScoreCutoff` | Significance or Z-score cutoff to mark outliers
`pch` | Integer or character to be used for plotting the points
`basePlot` | if `TRUE`, use the R base plot version, else use the plotly framework, which is the default
`col` | Set color for the points. If set, it must be a character vector of length 2. (1. normal point; 2. outlier point) or a single character referring to a color palette from `RColorBrewer`.
`global` | Flag to plot a global Q-Q plot, default FALSE
`samplePoints` | Sample points for Q-Q plot, defaults to max 30k points
`legendPos` | Set legendpos, by default topleft.
`outlierRatio` | The fraction to be used for the outlier sample filtering
`conf.alpha` | If set, a confidence interval is plotted, defaults to 0.05
`xlim, ylim` | The x/y limits for the plot or NULL to use the full data range
`log` | If TRUE, the default, counts are plotted in log10.
`groups` | A character vector containing either group assignments of samples or sample IDs. Is empty by default. If group assignments are given, the vector must have the same length as the number of samples. If sample IDs are provided the assignment will result in a binary group assignemt.
`groupColSet` | A color set from RColorBrewer or a manual vector of colors, which length must match the number of categories from groups.
`...` | Additional parameters passed to plot() or plot_ly() if not stated otherwise in the details for each plot function
`normalized` | If TRUE, the normalized counts are used, the default, otherwise the raw counts
`rowCentered` | If TRUE, the counts are row-wise (gene-wise) centered
`rowGroups, colGroups` | A vector of co-factors (colnames of colData) for color coding the rows. It also accepts a data.frame of dim = (#samples, #groups). Must have more than 2 groups.
`rowColSet, colColSet` | A color set from RColorBrewer/colorRampPalette
`nRowCluster, nColCluster` | Number of clusters to show in the row and column dendrograms. If this argument is set the resulting cluster assignments are added to the OutriderDataSet.
`nBreaks` | number of breaks for the heatmap color scheme. Default to 50.
`show_names` | character string indicating whether to show 'none', 'row', 'col', or 'both' names on the heatmap axes.
`bcvQuantile` | quantile for choosing the cutoff for the biological coefficient of variation (BCV)
`nGenes` | upper limit of number of genes (defaults to 500). Subsets the top n genes based on the BCV.
`yadjust` | Option to adjust position of Median and 90 percentile labels.
`ylab` | The y axis label
`bins` | Number of bins used in the histogram. Defaults to 100.
`compareDisp` | If TRUE, the default, and if the autoCorrect normalization was used it computes the dispersion without autoCorrect and plots it for comparison.

### Details

`plotAberrantPerSample`: The number of aberrant events per sample are plotted sorted by rank. The ... parameters are passed on to the `aberrant` function.

`plotVolcano`: the volcano plot is sample-centric. It plots for a given sample the negative log10 nominal P-values against the Z-scores for all genes.

`plotExpressionRank`: This function plots for a given gene the expression level against the expression rank for all samples. This can be used with normalized and unnormalized expression values.

`plotQQ`: the quantile-quantile plot for a given gene or if `global` is set to `TRUE` over the full data set. Here the observed P-values are plotted against the expected ones in the negative log10 space.

`plotExpectedVsObservedCounts`: A scatter plot of the observed counts against the predicted expression for a given gene.

`plotCountCorHeatmap`: The correlation heatmap of the count data of the full data set. Default the values are log transformed and row centered. This function returns an OutriderDataSet with annotated clusters if requested. The ... arguments are passed to the `pheatmap` function.

`plotCountGeneSampleHeatmap`: A gene x sample heatmap of the raw or normalized counts. By default they are log transformed and row centered. Only the top 500 viable genes based on the BCV (biological coefficient of variation) is used by default.

`plotSizeFactors`: The sizefactor distribution within the dataset.

`plotFPKM`: The distribution of FPKM values. If the OutriderDataSet object contains the `passedFilter` column, it will plot both FPKM distributions for the expressed genes and for the filtered genes.

`plotExpressedGenes`: A summary statistic plot on the number of genes expressed within this dataset. It plots the sample rank (based on the number of expressed genes) against the accumulated statistics up to the given sample.

`plotDispEsts`: Plots the dispersion of the OutriderDataSet model against the normalized mean count. If autoCorrect is used it will also estimate the dispersion without normalization for comparison.

`plotPowerAnalysis`: The power analysis plot should give the user a ruff estimate of the events one can be detected with OUTRIDER. Based on the dispersion of the provided OUTRIDER data set the theoretical P-value over the mean expression is plotted. This is done for different expression levels. The curves are smooths to make the reading of the plot easier.

`plotEncDimSearch`: Visualization of the hyperparameter optimization. It plots the encoding dimension against the achieved loss (area under the precision-recall curve). From this plot the optimum should be choosen for the `q` in fitting process.

### Value

If base R graphics are used nothing is returned else the plotly or the gplot object is returned.

### Examples

    ods <- makeExampleOutriderDataSet(dataset="Kremer")
    implementation <- 'autoencoder'

    mcols(ods)$basepairs <- 300 # assign pseudo gene length for filtering
    ods <- filterExpression(ods)
    ods <- OUTRIDER(ods, implementation=implementation)

    plotAberrantPerSample(ods)

    plotVolcano(ods, 49)
    plotVolcano(ods, 'MUC1365', basePlot=TRUE)

    plotExpressionRank(ods, 35)
    plotExpressionRank(ods, "NDUFS5", normalized=FALSE,
        log=FALSE, main="Over expression outlier", basePlot=TRUE)

    plotQQ(ods, 149)
    plotQQ(ods, global=TRUE, outlierRatio=0.001)

    plotExpectedVsObservedCounts(ods, 149)
    plotExpectedVsObservedCounts(ods, "ATAD3C", basePlot=TRUE)

    plotExpressedGenes(ods)

    sex <- sample(c("female", "male"), dim(ods)[2], replace=TRUE)
    colData(ods)$Sex <- sex
    ods <- plotCountCorHeatmap(ods, nColCluster=4, normalized=FALSE)
    ods <- plotCountCorHeatmap(ods, colGroup="Sex", colColSet="Set1")
    table(colData(ods)$clusterNumber_4)

    plotCountGeneSampleHeatmap(ods, normalized=FALSE)
    plotCountGeneSampleHeatmap(ods, rowGroups="theta",
            rowColSet=list(c("white", "darkgreen")))

    plotSizeFactors(ods)

    mcols(ods)$basepairs <- 1
    mcols(ods)$passedFilter <- rowMeans(counts(ods)) > 10
    plotFPKM(ods)

    plotDispEsts(ods, compareDisp=FALSE)

    plotPowerAnalysis(ods)

    ## Not run:
    # for speed reasons we only search for 5 different dimensions
    ods <- findEncodingDim(ods, params=c(3, 10, 20, 35, 50),
            implementation=implementation)
    plotEncDimSearch(ods)

    ## End(Not run)

[Package OUTRIDER version 1.6.0 Index]